Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32

Accurate and computationally light algorithms for estimating the state of charge (SoC) of a battery's cells are crucial for effective battery management on embedded systems. In this letter, we propose an adaptive extended Kalman filter (AEKF) for SoC estimation using a covariance adaptation tec...

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Bibliographic Details
Published in:IEEE embedded systems letters Vol. 17; no. 3; pp. 160 - 163
Main Authors: Barros, Antonio, Peretti, Edoardo, Fabroni, Davide, Carrera, Diego, Fragneto, Pasqualina, Boracchi, Giacomo
Format: Journal Article
Language:English
Published: IEEE 01.06.2025
Subjects:
Adaptive extended Kalman filter (AEKF)
Batteries
Computational modeling
Covariance matrices
Data structures
embedded implementation
Integrated circuit modeling
Kalman filters
Load modeling
Maximum likelihood estimation
State of charge
state of charge (SoC) estimation
STM32
Voltage measurement
ISSN:1943-0663, 1943-0671
Online Access:Get full text
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Summary:Accurate and computationally light algorithms for estimating the state of charge (SoC) of a battery's cells are crucial for effective battery management on embedded systems. In this letter, we propose an adaptive extended Kalman filter (AEKF) for SoC estimation using a covariance adaptation technique based on maximum likelihood estimation-a novelty in this domain. Furthermore, we tune a key design parameter-the estimation window size-to obtain an optimal memory-performance tradeoff, and experimentally demonstrate our solution achieves superior estimation accuracy with respect to existing alternative methods. Finally, we present a fully custom implementation of the AEKF for a general-purpose low-cost STM32 microcontroller, showing it can be deployed with minimal computational requirements adequate for real-world usage.
ISSN:1943-0663
1943-0671
DOI:10.1109/LES.2024.3489352